Machine learning-based speech recognition system for nursing documentation – A pilot study

• Published in: International journal of medical informatics (Shannon, Ireland) Vol. 178; p. 105213
• Main Authors: Lee, Tso-Ying, Li, Chin-Ching, Chou, Kuei-Ru, Chung, Min-Huey, Hsiao, Shu-Tai, Guo, Shu-Liu, Hung, Lung-Yun, Wu, Hao-Ting
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.10.2023
• Subjects: Artificial Intelligence; Dictation; Documentation; Humans; Natural language processing; Nursing Documentation; Perception; Pilot Projects; Speech; Speech Recognition; Speech Recognition Software; Speech Recognition; Natural language processing; Nursing Documentation; Artificial Intelligence; Dictation
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/j.ijmedinf.2023.105213

•A trained machine learning-based speech recognition (SR) system was developed and evaluated for its feasibility for nursing documentation.•The SR system may reduce the burden of documentation for nurses by providing flexibility in entry type, and the opportunity to record at point-of-care may improve workflow.•Further studies are needed to improve the integration of SR in the digital documentation of nursing records, in terms of both productivity and accuracy. Considering the significant workload of nursing tasks, enhancing the efficiency of nursing documentation is imperative. This study aimed to evaluate the effectiveness of a machine learning-based speech recognition (SR) system in reducing the clinical workload associated with typing nursing records, implemented in a psychiatry ward. The study was conducted between July 15, 2020, and June 30, 2021, at Cheng Hsin General Hospital in Taiwan. The language corpus was based on the existing records from the hospital nursing information system. The participating ward's nursing activities, clinical conversation, and accent data were also collected for deep learning-based SR-engine training. A total of 21 nurses participated in the evaluation of the SR system. Documentation time and recognition error rate were evaluated in parallel between SR-generated records and keyboard entry over 4 sessions. Any differences between SR and keyboard transcriptions were regarded as SR errors. Findings: A total of 200 data were obtained from four evaluation sessions, 10 participants were asked to use SR and keyboard entry in parallel at each session and 5 entries were collected from each participant. Overall, the SR system processed 30,112 words in 32,456 s (0.928 words per second). The mean accuracy of the SR system improved after each session, from 87.06% in 1st session to 95.07% in 4th session. This pilot study demonstrated our machine learning-based SR system has an acceptable recognition accuracy and may reduce the burden of documentation for nurses. However, the potential error with the SR transcription should continually be recognized and improved. Further studies are needed to improve the integration of SR in digital documentation of nursing records, in terms of both productivity and accuracy across different clinical specialties.